The Busara Blog
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The Busara Blog

The true cost of quick cash

Exploring biases and barriers in digital credit

Illustration by Lynette Gow

Digital credit — easily understood as lending through digital platforms and applications — has seen rapid uptake among consumers in recent years. Driven primarily by partnerships between mobile network operators and banks, the digital lending space is now swarmed with innovative offerings from new Fintech companies, micro-finance institutions, and non-bank financial companies. While access to quick cash is undoubtedly appealing to consumers, what meaningful impact is there to show for it? Is digital credit causing more harm than good? Over the next few weeks, our blog series will unpack biases and consumer behavior where digital credit is concerned, while highlighting key findings from a study on access to, and impact of, digital loans.

First things first though, what did we actually set out to do? Our researchers were curious about why digital credit is so expensive, despite presumably low operating costs. Consequently, a study was set up covering Kenya, Nigeria and India, to understand the influencing factors that lenders and consumers consider when offering and accessing digital credit, respectively.

Here’s where it gets interesting. The study found that borrowers are a fairly straightforward lot who — aside from cost of the loan (interest payable) — are equally or more concerned with access (fewer borrowing requirements), convenience (less paperwork and red tape), amount (increasing credit limits), and comfort (user friendly platforms and customer service).

Lenders, on the other hand, have riskier issues to consider, which ultimately drive up the cost of digital credit to the point of consumers defaulting on repayment (bad loans). Effective credit scoring, defined as the analysis to determine creditworthiness of a borrower, is a top concern. A typical credit scoring model would analyze a potential borrower’s credit history to determine likelihood of defaulting on a loan. However, given the fast-paced nature of digital lending, this elaborate screening is not always possible, leading to higher risk in lending. Alternative data sources, such as mobile phone records (think mPesa/ Tingg/ PayTM statements) and psychometric scoring, are out of reach for lenders. However, a regulatory shift towards consumer ownership of data and data portability offers the opportunity for customer-led data sharing.

Unlike traditional loans, digital credit does not offer the option to repay in installments. This unbending repayment schedule is strenuous on household or business expenditure, leading to high default rates. Flexible, customer-driven repayment models may be an effective way to simultaneously protect customers, and increase repayment rates.

Perhaps unsurprisingly, the study found a glaring gender gap in the uptake and usage of digital credit products, exacerbated by a number of factors. First, women are more likely to not have the basic requirements to access digital credit such as identification, phone ownership, and business licences. Secondly, male-dominated datasets lead to product innovation tailored to the needs of men. Finally, the study reveals that women self-select out of the market due to low confidence in their own creditworthiness, which is compounded by prevalent social norms.

The study offers recommendations of behavioral interventions which could go a long way in improving the digital credit experience for both lenders and consumers. These include:

  • Working with lenders and their intermediaries to help consumers make better credit choices by explaining terms and conditions in detail.
  • Supporting the creation of ideal data sharing environments where regulators, providers and other stakeholders can responsibly avail, store and distribute better data to stimulate market competition. Working with providers to explore transformation of loans from simply smoothing consumption, to being facilitators of serious investment and improved livelihoods.
  • Making providers aware of gender discrepancies in loan allocation and overall experience, where women are disadvantaged. Experimenting with alternatives to generic repayment structures can optimize the overall borrower experience. Working with regulators to prevent overly aggressive debt collection tactics.
  • Working with lenders to ensure that loan amounts match consumer needs, which can help prevent defaults and optimize impact.

This blog is the first in a series of blogs on digital credit. Follow us on social media to be notified as soon as the next blog in the series is out. If you liked this, head over to our YouTube channel to watch the corresponding Part 1 video on our Digital Credit study.

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Busara is a research and advisory firm dedicated to advancing Behavioral Science in the Global South